1. Field of the Invention
The present invention relates to digital medical image analysis, particularly by means of a computer-implemented algorithm.
2. Background
Medical images are a valuable tool for detection, diagnosis and evaluation of response to treatment of disease, as well as surgical planning. A variety of physical techniques or modalities have been developed for producing these images, including projection X-rays, computed tomography (CT), ultrasound (US), positron emission tomography (PET) and magnetic resonance imaging (MRI). The images may be generated digitally (using, e.g., US, CT or MRI) or digitized from an analog image (e.g., film). Conventionally, trained radiologists or other clinicians review these images to facilitate detection of an abnormality, for example. A radiologist or clinician can review and annotate the digitized images and generate a report based on the review. All of the resultant data may be stored for later retrieval and analysis.
The task of a user (e.g., radiologist, clinician, etc.) can be made easier by reviewing the images with application software providing visualization and analysis tools to manipulate and evaluate these images in 2, 3 and 4 dimensions (for images that vary in time). However, the evaluation process can result in missed abnormalities because of normal limitations in human perception. This perception issue is worsened by the ever-increasing amount of information now available to the radiologist or clinician for review. Now, in the information-intense but resource- and time-constrained environments in which radiologists work, they are forced to make expedited decisions, potentially resulting in increased miss rate.
Computer Assisted Detection or Diagnosis (CAD) software has been designed to reduce errors of human perception, as well as to enhance the productivity of radiologists or other clinicians in an information-intense environment, by automatically performing for the user the more mundane tasks (e.g., automatic measurement) and focusing the radiologist's limited time on interpretation. CAD software can automatically or semi-automatically detect and measure abnormalities, characterize abnormalities, measure temporal progression or regression of disease and surgically plan based on CAD information. For example, the applicant's MedicHeart™, MedicLung™ and MedicColon™ diagnostic software perform semiautomatic and automatic analysis of CT scans of the heart, lung and colon, respectively.
CAD software uses algorithms to analyze a given medical image. No one algorithm is robust enough to analyze accurately all types of medical images. For example, abnormal structures in the lung have different image characteristics from those in the colon. Images acquired using different modalities or combinations of modalities (e.g., MRI, CT, US) have different resolutions and image characteristics, and hence require more specific algorithms to analyze them. Given a choice of CAD algorithms designed to more closely address the needs of a specific disease state or acquisition modality, the user of CAD software would likely opt for that algorithm designed more specifically (e.g., the clinical condition, modality or focused anatomy of the dataset) and select the appropriate algorithm for analysis of that type of image. Alternatively, the user may only be interested in analyzing one type of image and therefore only use one type of algorithm.
U.S. Pat. No. 5,235,510 to Yamada et al. describes a system for automating the selection of an appropriate algorithm for the analysis of a medical image by inspecting attribute data, identifying the image type and selecting an algorithm appropriate for that type.
Many CAD algorithms rely on a predefined set of parameter values for detection. For example, the Agatston method, as originally described in “Quantification of Coronary Artery Calcium Using Ultrafast Computed Tomography”, Agatston A S, Janowitz W R, Hildner F J et al., J Am Coll Cardiol 1990 15: 827–832 (hereinafter “the Agatston article”), applies a threshold of 130 Hounsfield units (HU) to the CT image, and identifies all pixels above that threshold as containing calcium. A scoring system is then used to rate the severity of the calcification, based on the number of pixels above the threshold multiplied by a weight based on the highest intensity within the calcification. If the highest intensity is between 130 and 200 HU, then the weight is 1. If the highest intensity is between 200 and 300 HU, the weight is 2. If the highest intensity is greater than 300 RU, the weight is 3. The threshold of 130 HU works reasonably well with the types of CT scan images available at the time of publication of the Agatston article, but there is no general agreement as to how this threshold should be modified for new types of CT scan, such as data acquired with thinner collimation.
Alternatively, the CAD application software may allow the user to set the values of parameters used in the analysis. For example, CAR® software (available from Medicsight PLC, a company located in London, England), aspects of which are described in a prior patent application GB0420147.1 to Dehmeshki, provides a user interface allowing the user to interactively modify the parameters used by an algorithm. The results of any selected parameters are available to the user. While this user interaction provides great flexibility, the optimum parameter values may not be known. For example, the user may select a less optimal parameter value for analysis. In another example, the user may select the parameter value by trial and error, further impacting productivity.
Using predefined parameter values has the advantage of simplicity and consistency, but may not always provide better results, as compared to using parameter values that are not predefined. While user-defined parameter values provide greater flexibility, they may not provide better results unless the optimum parameter values are chosen. Allowing the user to set parameter values adds to the complexity of CAD software.
U.S. Pat. No. 6,058,322 describes an interactive user modification function in which the software displays detected microcalcifications, and the user may then add or delete microcalcifications. The software modifies its estimated likelihood of malignancy accordingly.
EP-A-1398722 describes a “dynamic CAD” system in which an image is processed to identify features of interest and to extract parameters from the features of interest. The image is post-processed using the extracted parameters to generate a second image. The post-processing parameters are derived from the image itself, rather than from metadata associated with the image.
U.S. Pat. No. 5,878,746 describes a computerized diagnostic system, which may process medical images to derive feature vectors. The system inputs the feature vectors together with other clinical parameters into a “fact database”, which is then processed to obtain a diagnosis.